| 岳胜如,胡雪菲,侯晓华,孟福军,冯卓亚.未来气候变化对新疆阿拉尔棉花产量和灌溉量的影响[J].干旱地区农业研究,2025,(6):292~305 |
| 未来气候变化对新疆阿拉尔棉花产量和灌溉量的影响 |
| Impact of future climate change on cotton yield and irrigation amount in Alaer, Xinjiang |
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| DOI：10.7606/j.issn.1000-7601.2025.06.28 |
| 中文关键词: 棉花 气候变化 灌溉量 产量 全球气候模型 DSSAT-CROPGRO-Cotton模型 |
| 英文关键词:cotton climate change irrigation amount yield global climate model DSSAT-CROPGRO-cotton model |
| 基金项目:新疆生产建设兵团科技计划项目（2024AB064）；塔里木大学校长基金胡杨英才（硕士）项目（TDZKSS202405） |
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| 中文摘要: |
| 预测和评估未来气候多变性对干旱区棉花产量和所需灌溉量的潜在影响至关重要。基于13种全球气候模型（GCMs）驱动参数本地化的DSSAT-CROPGRO-Cotton模型，模拟了SSP2-4.5和SSP5-8.5情景下棉花产量和灌溉量的变化趋势，并且考虑了GCMs间差异对模拟结果的影响。利用相关性分析、随机森林算法和逐步回归分析方法，探究了各气候因素对产量和灌溉量的影响机制。结果表明：（1）13种GCMs对阿拉尔未来降水模拟的不确定性最高，其次是辐射，温度模拟的不确定性最小。（2）SSP2-4.5和SSP5-8.5情景下棉花产量分别平均增加23.0%和22.7%，其中CO2浓度变化在其中的贡献分别为14.9%和23.4%，CO2浓度变化对灌溉量的影响有限；不同GCMs下的棉花产量和灌溉量的模拟结果存在较大差异，SSP5-8.5情景下，在2090s时期，INM-CM4.8和IPSL-CM6A-LR模型的产量模拟结果差异高达102.6%。（3）未来情景下棉花产量和灌溉量的主要影响因素及其特征权重随时间变化，SSP2-4.5情景下，温度和CO2浓度与产量随着时间推移由正相关变为负相关；SSP5-8.5情景下，这种转变更早且更显著；最高温度、降水和辐射是影响灌溉量的关键因素。 |
| 英文摘要: |
| Predicting and assessing the potential impacts of future climate variability on cotton yield and irrigation requirements in arid regions is of vital importance. This study employed the DSSAT-CROPGRO-Cotton model (Decision Support System for Agrotechnology Transfer-CROPGRO Cotton), driven by parameters localized from 13 Global Climate Models (GCMs), to simulate the trends in cotton yield and irrigation amount under the Shared Socioeconomic Pathways (SSP) scenarios of SSP2-4.5 and SSP5-8.5. The study also considered the influence of inter\|model differences in climate models on the simulation results. Through correlation analysis, random forest algorithms, and stepwise regression analysis, we qualitatively and quantitatively evaluated the mechanisms by which various climate factors affect yield and irrigation requirements. The results showed that (1) the uncertainty of 13 GCMs for future precipitation simulation in Alar is the highest, followed by radiation, and the uncertainty of temperature simulation is the smallest. (2) Under the SSP2-4.5 and SSP5-8.5 scenarios, cotton yield increased by 23.0% and 22.7% on average, and the contribution of CO2 concentration change was 14.9% and 23.4%, respectively. The impact of CO2 concentration change on irrigation amount was limited. There were significant differences in yield and irrigation amount simulation results across different GCMs. Under the SSP5-8.5 scenario, in the 2090s, the yield simulation results from the INM-CM4.8 and IPSL-CM6A-LR models differed by as much as 102.6%. (3) Under future scenarios, the key factors affecting cotton yield and irrigation amount, as well as their characteristic weights, changed over time. Under the SSP2-4.5 scenario, temperature and CO2 concentration shifted from a positive to a negative correlation with yield over time. Under the SSP5-8.5 scenario, this shift happened earlier and was more pronounced. Maximum temperature, precipitation, and radiation were the main factors influencing irrigation amount. |
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